
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__lowerCAmelCase     :	Any       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	Optional[Any]       			=  {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""t5"""
  a__       			=						["""past_key_values"""]
  a__       			=						{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
  def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					List[str]=3_2128     ,				UpperCamelCase__     :					Any=512     ,				UpperCamelCase__     :					int=64     ,				UpperCamelCase__     :					Tuple=2048     ,				UpperCamelCase__     :					List[str]=6     ,				UpperCamelCase__     :					List[str]=None     ,				UpperCamelCase__     :					List[Any]=8     ,				UpperCamelCase__     :					Any=32     ,				UpperCamelCase__     :					List[str]=128     ,				UpperCamelCase__     :					List[str]=0.1     ,				UpperCamelCase__     :					Any=1E-6     ,				UpperCamelCase__     :					Optional[Any]=1.0     ,				UpperCamelCase__     :					List[Any]="relu"     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					Optional[Any]=True     ,				UpperCamelCase__     :					Any=0     ,				UpperCamelCase__     :					Optional[Any]=1     ,				**UpperCamelCase__     :					Any     ,				)     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			d_kv
    __magic_name__								=			d_ff
    __magic_name__								=			num_layers
    __magic_name__								=			(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    __magic_name__								=			num_heads
    __magic_name__								=			relative_attention_num_buckets
    __magic_name__								=			relative_attention_max_distance
    __magic_name__								=			dropout_rate
    __magic_name__								=			layer_norm_epsilon
    __magic_name__								=			initializer_factor
    __magic_name__								=			feed_forward_proj
    __magic_name__								=			use_cache

    __magic_name__								=			self.feed_forward_proj.split("""-"""       )
    __magic_name__								=			act_info[-1]
    __magic_name__								=			act_info[0] == """gated"""

    if len(UpperCamelCase__       ) > 1 and act_info[0] != "gated" or len(UpperCamelCase__       ) > 2:
      raise ValueError(
          F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
          """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
          """'gated-gelu' or 'relu'"""       )

    # for backwards compatibility
    if feed_forward_proj == "gated-gelu":
      __magic_name__								=			"""gelu_new"""

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				is_encoder_decoder=UpperCamelCase__     ,				**UpperCamelCase__     ,				)







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  @property
  def 				_lowercase				(					self     :					Dict       )     ->      Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    __magic_name__								=			{
        """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
        """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
    }
    if self.use_past:
      __magic_name__								=			"""past_encoder_sequence + sequence"""
      __magic_name__								=			{0: """batch"""}
      __magic_name__								=			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
    else:
      __magic_name__								=			{0: """batch""", 1: """decoder_sequence"""}
      __magic_name__								=			{0: """batch""", 1: """decoder_sequence"""}

    if self.use_past:
      self.fill_with_past_key_values_(UpperCamelCase__     ,				direction="""inputs"""       )

    return common_inputs





  @property
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      int:







    """simple docstring"""






    return 13



from __future__ import annotations

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __magic_name__	,       __magic_name__								=			array[indexa], array[indexa]

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    for i in range(A_,	low + middle   ):
      comp_and_swap(A_,	A_,	i + middle,	A_   )
    bitonic_merge(A_,	A_,	A_,	A_   )
    bitonic_merge(A_,	low + middle,	A_,	A_   )

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    bitonic_sort(A_,	A_,	A_,	1   )
    bitonic_sort(A_,	low + middle,	A_,	0   )
    bitonic_merge(A_,	A_,	A_,	A_   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Optional[Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	List[Any]       			=  [int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')

